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1

Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

Muralidhar Rathan, Hulipalled Vishwanath R.

Foundations of Computing and Decision Sciences

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2023

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Vol. 48, No. 2

181--209

EN

Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.

2

Novel framework for aspect knowledge base generated automatically from social media using pattern rules

Tran Tuan Anh, Duangsuwan Jarunee, Wettayaprasit Wiphada

Computer Science

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2021

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T. 22 (4)

489--516

EN

One of the factors that improve businesses in business intelligence is summarization systems that can generate summaries based on sentiment from social media. However, these systems cannot produce such summaries automatically; they use annotated datasets. To support these systems with annotated datasets, we propose a novel framework that uses pattern rules. The framework has two procedures: 1) pre-processing, and 2) aspect knowledge-base generation. The first procedure is to check and correct any misspelled words (bigram and unigram) by a proposed method and tag the parts-of-speech of all of the words. The second procedure is to automatically generate an aspect knowledge base that is to be used to produce sentiment summaries by sentiment-summarization systems. Pattern rules and semantic similarity-based pruning are used to automatically generate an aspect knowledge base from social media. In the experiments, eight domains from benchmark datasets of reviews are used. The performance evaluation of our proposed approach shows the highest performance when compared to other unsupervised approaches.

3

Aspect-based sentiment classification model employing whale-optimized adaptive neural network

Balaganesh Nallathambi, Muneeswaran K.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e137271

EN

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.

4

Customer product review summarization over time for competitive intelligence

Amarouche Kamal, Benbrahim Houda, Kassou Ismail

Journal of Automation Mobile Robotics and Intelligent Systems

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2018

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Vol. 12, No. 4

70--82

EN

Nowadays, Customer’s product reviews can be widely found on the Web, be it in personal blogs, forums, or ecommerce websites. They contain important products’ information and therefore became a new data source for competitive intelligence. On that account, these reviews need to be analyzed and summarized in order to help the leader of an entity (company, brand, etc.) to make appropriate decisions in an efective way. However, most previous review summarization studies focus on summarizing sentiment distribution toward different product features without taking into account that the real advantages and disadvantages of a product clarify over time. For this reason, in this work we aim to propose a new system for product opinion summarization which depends on the time when reviews are expressed and that covers the sentiments change about product features. The proposed system firstly, generates a summary based on product features in order to give more accurate and efficient information about different features. secondly, classify the product based on its features in its appropriate class (good, medium or bad product) using a fuzzy logic system. The experimental results demonstrate the effectiveness of the proposed system to generate the real image of a product and its features in reviews.

5

Information management tools for innovation analysts

Eito-Brun R.

Problemy Eksploatacji

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2014

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no. 4

73--82

EN

Innovation management is a knowledge-intensive process that requires dealing with different sources of data to identify relationships between the concepts, techniques, and tools that may led to innovations. Innovation analysts need to handle huge amounts of unstructured information: ideas gathered from internal staff and external partners, research papers and technical reports, patents and applications, etc. All these sources constitute valid inputs to assess the innovativeness of ideas, the feasibility of their implementation, and their potential value in the market. Innovation management discipline has widely used techniques and methods developed in the context of Information Science to support the identification of research trends, assess the outputs of innovation efforts and investments, and monitor the market and the activities made by competitors. The fruitful relationship between Information Science techniques and Innovation management needs to be regularly reviewed as new techniques and tools are designed and made available to the community. In the last years, significant progress has been achieved in areas like scientometrics, text visualization, and opinion mining. This paper provides an overview of these techniques and discusses how they can help professionals involved in innovation programs.

PL

Zarządzanie innowacjami to oparty na wiedzy proces, w którym definiowany jest poziom zależności pomiędzy pomysłami, technikami i narzędziami mogącymi skutkować opracowaniem innowacji. Analityk innowacji musi zarządzać treściami niestrukturalnymi: pomysłami zgromadzonymi od pracowników jak i partnerów, wiedzą pochodzącą z publikacji naukowych i raportów technicznych, patentami i zgłoszeniami patentowymi itp. Wszystkie te źródła stanowią istotny wkład w proces oceny innowacyjności pomysłu, możliwości jego realizacji oraz konkurencyjności rynkowej. W zarządzaniu innowacjami powszechnie stosowane są techniki i metody informatyczne, które wspomagają proces identyfikacji trendów, oceny rezultatów, oszacowania niezbędnych nakładów finansowych czy monitorowania rynku. Oznacza to, że należy regularnie monitorować stan wiedzy i techniki w tym obszarze w celu zapewnienia jak najbardziej owocnej współpracy na styku nauk informatycznych i zarządzania innowacjami. W ostatnich latach znaczący postęp osiągnięto w takich dziedzinach jak naukometria, wizualizacja tekstu i badanie opinii. W artykule dokonano przeglądu tych technik i omówiono sposób, w jaki mogą one wspomóc specjalistów zaangażowanych w realizację innowacyjnych programów.

6

Performance of k-nearest neighbors algorithm in opinion classification

Jędrzejewski K., Zamorski M.

Foundations of Computing and Decision Sciences

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2013

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Vol. 38, No. 2

97-110

EN

This paper presents another approach for determining document’s semantic orientation process. It includes a brief introduction describing the area of application of opinion mining, and some definitions useful in the field. The most commonly used methods are mentioned and some alternative ones are described. Experiment results are presented which show that kNN algorithm gives similar results to proportional algorithm.